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            <h1 class="title is-1 publication-title">OmniGlue: Generalizable Feature Matching with Foundation Model Guidance</h1>
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                <a href="https://hwjiang1510.github.io/" target="_blank">Hanwen Jiang</a><sup>1</sup>,</span>
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                  <a href="https://scholar.google.com/citations?user=jgSItF4AAAAJ&hl=en" target="_blank">Arjun Karpur</a><sup>2</sup>,</span>
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                  <a href="https://scholar.google.com/citations?user=7EeSOcgAAAAJ&hl=en" target="_blank">Bingyi Cao</a><sup>2</sup>,</span>
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                  <a href="https://www.cs.utexas.edu/~huangqx/index.html" target="_blank">Qixing Huang</a><sup>1</sup>,</span>
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                    <a href="https://andrefaraujo.github.io/" target="_blank">Andre Araujo</a><sup>2</sup>
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                    <span class="author-block"><sup>1</sup>UT Austin&nbsp;&nbsp;&nbsp;&nbsp; <sup>2</sup>Google Research<br>CVPR 2024</span>
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        <h2 class="title is-3">Abstract</h2>
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            The image matching field has been witnessing a continuous emergence of novel learnable feature matching techniques, with ever-improving performance on conventional benchmarks.
			However, our investigation shows that despite these gains, their potential for real-world applications is restricted by their limited generalization capabilities to novel image domains.
			In this paper, we introduce OmniGlue, the first learnable image matcher that is designed with generalization as a core principle.
			OmniGlue leverages broad knowledge from a vision foundation model to guide the feature matching process, boosting generalization to domains not seen at training time.
			Additionally, we propose a novel keypoint position-guided attention mechanism which disentangles spatial and appearance information, leading to enhanced matching descriptors.
			We perform comprehensive experiments on a suite of 6 datasets with varied image domains, including scene-level, object-centric and aerial images.
			OmniGlue's novel components lead to relative gains on unseen domains of 20.9% with respect to a directly comparable reference model SuperGlue, while also outperforming the recent LightGlue method by 9.5% relatively.
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                OmniGlue is the first learnable image matcher that is de-signed with generalization as a core principle. 
				OmniGlue benefits from two designs: foundation model guidance and keypoint-position attention guidance. 
				The visual foundation model, which is trained on large-scale data, provides coarse but generalizable correspondence cues. It huides the inter-image feature propagation process.
				The keypoint-position attention guidance disentangles the positional informatation from the keypoint features, which avoids the model specializing too strongly in the training dis-tribution of keypoints and relative pose transformations.
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      <pre><code>@inproceedings{jiang2024Omniglue,
   title={OmniGlue: Generalizable Feature Matching with Foundation Model Guidance},
   author={Jiang, Hanwen and Karpur, Arjun and Cao, Bingyi and Huang, Qixing and Araujo, Andre},
   booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   year={2024},
}</code></pre>
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